What is this about and what can I find here?

Overview

The classification and diagnosis of central nervous system (CNS) diseases have long been constrained by traditional phenotypic approaches that fail to capture the underlying pathophysiological mechanisms, molecular biomarkers, and neuroanatomical changes that drive disease progression. For instance, neurodegenerative and psychiatric disorders exhibit significant clinical overlap, co-pathology, and heterogeneity, a new diagnostic framework is urgently needed—one that shifts from symptom-based classifications toward an etiology-driven, tridimensional system integrating genetics, proteomics, neuroimaging, and computational modeling. By leveraging AI, multi-modal biomarkers, and precision medicine, this framework aims to provide a more objective, scalable, and biologically grounded approach to diagnosing and managing CNS diseases, ultimately leading to earlier detection, personalized interventions, and improved patient outcomes. 

The project aims to develop a tridimensional diagnostic framework with an AI-powered annotation system, integrating etiology, molecular biomarkers, and neuroanatomoclinical correlations for precise and scalable CNS disease diagnostics.

The Tridimensional Diagnostic Framework redefines CNS diseases can be classified and diagnosed by focusing on:

• Axis 1: Etiology (genetic or other causes of diseases).
• Axis 2: Molecular Markers (biomarkers).
• Axis 3: Neuroanatomoclinical correlations (linking clinical symptoms to structural changes in the nervous system).

This methodology enables:

• Greater precision in diagnosis.
• Integration of incomplete datasets using AI-driven probabilistic modeling.
• Stratification of patients for personalized treatment.

The case of neurodegenerative diseases

There have been described these 3 diagnostic axes:

• Axis 1: Etiology

  • Description: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
  • Examples: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
  • Tests: Genetic testing, lifestyle, and cardiovascular screening.

• Axis 2: Molecular Markers

  • Description: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
  • Examples: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
  • Tests: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).

• Axis 3: Neuroanatomoclinical

  • Description: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments.
  • Examples: Hippocampal atrophy correlating with memory deficits.
  • Tests: MRI volumetrics, FDG-PET, neuropsychological evaluations.

Applications

This system enhances:

• Research: By stratifying patients, reduces cohort heterogeneity in clinical trials.
• Clinical Practice: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.

Who has access?

We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!

How to Contribute

• Access the `/docs` folder for guidelines.
• Use `/code` for the latest AI pipelines.
• Share feedback and ideas in the wiki discussion pages.

Key Objectives

• Develop interpretable AI models for diagnosis and progression tracking. 
• Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
• Foster collaboration among neuroscientists, AI researchers, and clinicians.

Main contents

• `/docs`: Documentation and contribution guidelines.
• `/code`: Machine learning pipelines and scripts.
• `/data`: Sample datasets for testing.
• `/outputs`: Generated models, visualizations, and reports.
• Methodology
• Results
• to-do-list